Method and apparatus for identifying potentially misclassified arrhythmic episodes

ABSTRACT

An implantable cardiac device is configured to classify cardiac arrhythmias using a plurality of arrhythmia discrimination algorithms. Data is provided that is associated with a plurality of cardiac arrhythmic episodes for which a cardiac electrical therapy was delivered or withheld by the implantable medical device based on the plurality of arrhythmia discrimination algorithms. A metric for each of the arrhythmic episodes is computed. The metric defines a measure by which the implantable cardiac device properly classified the arrhythmia. Potentially misclassified arrhythmic episodes of the plurality of cardiac arrhythmic episodes for which cardiac electrical therapy was inappropriately delivered or withheld are algorithmically identified using the metric.

RELATED APPLICATIONS

This application claims the benefit of Provisional Patent ApplicationSer. No. 60/844,253, filed on Sep. 13, 2006, to which priority isclaimed pursuant to 35 U.S.C. §119(e) and which is hereby incorporatedherein by reference.

FIELD OF THE INVENTION

The present invention relates generally to cardiac systems and methods,and, more particularly, to systems and method for identifying cardiacarrhythmias that are potentially misclassified.

BACKGROUND OF THE INVENTION

Proper cardiac function relies on the synchronized contractions of theheart at regular intervals. When normal cardiac rhythm is initiated atthe sinoatrial node, the heart is said to be in sinus rhythm. However,due to electrophysiologic disturbances caused by a disease process orfrom an electrical disturbance, the heart may experience irregularitiesin its coordinated contraction. In this situation, the heart is denotedto be arrhythmic. The resulting cardiac arrhythmia impairs cardiacefficiency and can be a potential life threatening event.

Cardiac arrhythmias occurring in the atria of the heart, for example,are called supra-ventricular tachyarrhythmias (SVTs). SVTs take manyforms, including atrial fibrillation and atrial flutter. Both conditionsare characterized by rapid, contractions of the atria. Cardiacarrhythmias occurring in the ventricular region of the heart, by way offurther example, are called ventricular tachyarrhythmias. Ventriculartachyarrhythmias (VTs), are conditions denoted by a rapid heart beat,150 to 250 beats per minute, originating from a location within theventricular myocardium. Ventricular tachyarrhythmia can quicklydegenerate into ventricular fibrillation (VF). Ventricular fibrillationis a condition denoted by extremely rapid, non synchronous contractionsof the ventricles. This condition is fatal unless the heart is returnedto sinus rhythm within a few minutes.

Implantable cardioverter/defibrillators (ICDS) have been used as aneffective treatment for patients with serious tachyarrhythmias. ICDs areable to recognize and treat tachyarrhythmias with a variety of tieredtherapies. These tiered therapies range from providing anti-tachycardiapacing pulses or cardioversion energy for treating tachyarrhythmias tohigh energy shocks for treating ventricular fibrillation. To effectivelydeliver these treatments, the ICD must first identify the type oftachyarrhythmia that is occurring, after which appropriate therapy maybe provided to the heart.

For the reasons stated above, and for other reasons stated below whichwill become apparent to those skilled in the art upon reading thepresent specification, there is a need in the art for reliably andaccurately recognize types of cardiac rhythms produced by the heart. Thepresent invention fulfills these and other needs.

SUMMARY OF THE INVENTION

The present invention is directed to methods and systems for identifyingcardiac arrhythmias that are potentially misclassified. According tovarious embodiments, methods and systems of the present inventionprovide for identification of cardiac arrhythmias that are potentiallymisclassified by a cardiac device implanted in a patient. Theimplantable cardiac device is configured to classify cardiac arrhythmiasusing a number of arrhythmia discrimination algorithms. Data is providedthat is associated with a number of cardiac arrhythmic episodes forwhich a cardiac electrical therapy was delivered or withheld by theimplantable cardiac device based on the arrhythmia discriminationalgorithms.

A metric is computed for each of the arrhythmic episodes. The metricdefines a measure by which the implantable cardiac device properlyclassified the arrhythmia. Methods and system further provide foralgorithmically identifying potentially misclassified arrhythmicepisodes of the cardiac arrhythmic episodes for which cardiac electricaltherapy was inappropriately delivered or withheld using the metric.Algorithmically identifying potentially misclassified arrhythmicepisodes may involve comparing metric values of the cardiac arrhythmicepisodes to a threshold or a threshold range.

The metric may define a probability of incorrectly classifying a cardiacarrhythmic episode, such as a probability of incorrectly classifying aventricular tachycardia episode. The metric may be reflective of theaccuracy by which each arrhythmia discrimination algorithm implementedby the implantable cardiac device properly classified an arrhythmicepisode. The metric may be based on a product of the probabilities ofthe arrhythmia discrimination algorithms implemented by the implantablecardiac device to incorrectly classify an arrhythmic episode.

The data and metrics associated with the cardiac arrhythmic episodes maybe arranged in a logbook format. The potentially misclassifiedarrhythmic episodes may be displayed for clinician review. For example,the data may be sorted based on the metrics, and the sorted data andmetrics may be displayed for clinician review. The manner in which thedata and metrics associated with the potentially misclassifiedarrhythmic episodes are displayed may be varied in accordance with adegree of potential misclassification.

Computing the metric and algorithmically identifying potentiallymisclassified arrhythmic episodes may be respectively performed by adevice external to the patient. For example, the data may be provided toa processor external of the patient. The external processor mayimplement one or more arrhythmia discrimination algorithms to classifythe cardiac arrhythmias based on the data. The metric may represent ameasure of certainty that the cardiac arrhythmia classification ofarrhythmic episodes respectively made by the implantable cardiac deviceand the external processor are in agreement.

Methods and systems may provide for adjustment of the metric in responseto clinician input and/or adjusting a parameter of one or more of thearrhythmia discrimination algorithms. Arrhythmic episodes may be flaggedfor clinician review in response to the metric failing to exceed acertainty threshold. One or more of the arrhythmia discriminationalgorithms of the implantable cardiac device may be modified in responseto the metric failing to exceed a certainty threshold.

In some implementations, the arrhythmia discrimination algorithms mayinclude an algorithm that compares a morphology of a cardiac signal to asupraventricular tachycardia (SVT) template. An SVT template may beautomatically generated for arrhythmic episodes for which the cardiacarrhythmia classifications respectively made by the implantable cardiacdevice and the external processor are in disagreement but the metricmeets or exceeds a certainty threshold.

In other implementations, the arrhythmia discrimination algorithms mayinclude an algorithm that compares a morphology of a cardiac signal to asupraventricular tachycardia (SVT) template. Arrhythmic episodes forclinician review may be flagged in response to the metric failing tomeet or exceed a certainty threshold. An SVT template may be generatedfor selected ones of the flagged arrhythmic episodes in response to aclinician input.

Systems of the present invention may include an implantable cardiacdevice configured to classify cardiac arrhythmias using a number ofarrhythmia discrimination algorithms. A memory preferably stores dataassociated with a number of cardiac arrhythmic episodes for which acardiac electrical therapy was delivered or withheld by the implantablecardiac device based on the arrhythmia discrimination algorithms.

A processor may be configured to compute a metric for each of thearrhythmic episodes, the metric defining a measure by which theimplantable cardiac device properly classified the arrhythmia. Theprocessor may be configured to algorithmically identify potentiallymisclassified arrhythmic episodes of the cardiac arrhythmic episodes forwhich cardiac electrical therapy was inappropriately delivered orwithheld using the metric. In some implementations, one or both of thememory and processor is disposed in the implantable cardiac device. Inother implementations, one or both of the memory and processor isdisposed in a patient-external device.

The above summary of the present invention is not intended to describeeach embodiment or every implementation of the present invention.Advantages and attainments, together with a more complete understandingof the invention, will become apparent and appreciated by referring tothe following detailed description and claims in conjunction with theaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A shows a typical report of an arrhythmia logbook that presentsvarious data associated with a number of arrhythmic episodes;

FIG. 1B shows a report of an arrhythmia logbook that presents variouscardiac rhythm data and a validation metric associated with each of thearrhythmic episodes in accordance with embodiments of the presentinvention;

FIG. 2A is a treatment decision table arranged in a conventional mannerfor use by an implantable cardiac device;

FIG. 2B is a treatment decision table arranged based on validationmetric values for use by an implantable cardiac device or an externalprocessor in accordance with embodiments of the present invention;

FIG. 2C is a partial showing of an arrhythmia logbook report with datapresented based on a validation metric sort in accordance withembodiments of the present invention;

FIG. 3 is a flow chart that characterizes a validation metric-baseddiscrimination algorithm of the present invention;

FIG. 4A is a flow chart that characterizes a morphology-based rhythmidentification algorithm according to embodiments of the presentinvention;

FIG. 4B describes various operations performed by the morphology-basedrhythm identification algorithm shown in FIG. 4A;

FIG. 5 illustrates a method of improving the performance of amorphology-based tachyarrhythmia discrimination algorithm implemented byan implantable cardiac device by use of patient-external computationalresources in accordance with embodiments of the present invention;

FIG. 6 is a diagram of an implantable cardiac device with leadsimplanted within a patient's heart, the implantable cardiac deviceimplementing discrimination algorithms in accordance with embodiments ofthe present invention; and

FIG. 7 is a block diagram of various components of an implantablecardiac device that implements discrimination algorithms in accordancewith embodiments of the present invention.

While the invention is amenable to various modifications and alternativeforms, specifics thereof have been shown by way of example in thedrawings and will be described in detail below. It is to be understood,however, that the intention is not to limit the invention to theparticular embodiments described. On the contrary, the invention isintended to cover all modifications, equivalents, and alternativesfalling within the scope of the invention as defined by the appendedclaims.

DESCRIPTION OF VARIOUS EMBODIMENTS

In the following description of the illustrated embodiments, referencesare made to the accompanying drawings forming a part hereof, and inwhich are shown, by way of illustration, various embodiments by whichthe invention may be practiced. It is to be understood that otherembodiments may be utilized, and structural and functional changes maybe made without departing from the scope of the present invention.

Systems, devices or methods according to the present invention mayinclude one or more of the features, structures, methods, orcombinations thereof described herein. For example, a device or systemmay be implemented to include one or more of the advantageous featuresand/or processes described below. It is intended that such device orsystem need not include all of the features described herein, but may beimplemented to include selected features that provide for usefulstructures and/or functionality. Such a device or system may beimplemented to provide a variety of therapeutic or diagnostic functions.

The present invention is directed to methods and systems for analyzingdata associated with cardiac rhythms and, in particular, cardiacarrhythmias such as tachycardia. Methods and systems of the presentinvention may be implemented to analyze cardiac rhythm data forassessing whether an arrhythmic episode, such as a tachycardia episode,has been properly classified. Methods and systems of the presentinvention may be implemented to analyze cardiac rhythm data forassessing whether a cardiac therapy delivered to, or inhibited from, apatient was appropriate. Such analyses may be performed by animplantable device, an external device, or a combination of both, fullyautomatically or with assistance/input from a clinician or physician.

Analysis results may be used to modify device parameters that influencediagnosis and/or treatment of cardiac arrhythmias by the implantabledevice. Such modifications may be implemented autonomously (i.e.,algorithmically) without user input or may involve input from a user.Analyses and modification to device parameters may be performed withinan implantable device, by a processing device external to the patient,or a combination of both.

According to embodiments of the invention, the complexity associatedwith conventional methods of analyzing cardiac rhythm data by a user issignificantly reduced by implementation of a validation metric. Avalidation metric of the present invention may be used in a variety ofapplications, including verification of device diagnostics and therapydecisions, troubleshooting and modifying device diagnostics andtherapies, triggering initiation of diagnostics and/or therapies, andremote system/user reporting and alerting function, among others.

A validation metric of the present invention represents an easilyunderstandable index that may be used to assess (e.g., filter) the verydetailed information obtained by diagnostic algorithms operative in mostimplantable cardiac devices. In particular embodiments, a validationmetric represents a measure by which an implantable cardiac deviceproperly classified an arrhythmia. A validation metric may be used toidentify potentially misclassified arrhythmias for which cardiacelectrical therapy was improperly delivered or withheld. A validationmetric of the present invention may be used by an implantable cardiacdevice for making therapy decisions.

Features and advantages of a validation metric of the present inventioncan be readily appreciated in the context of cardiac rhythm data that isstored or presented in a tabular or matrix format, such as a logbookformat as is known in the art. It is understood that a validation metricof the present invention may be used in a variety of manners, and thatuse in the context of an arrhythmia logbook represents one of many suchapplications. For example, and as will be discussed hereinbelow, avalidation metric or other validation algorithm may be implemented by apatient-external processing system to verify the appropriateness ofarrhythmia classifications made by an implantable cardiac device, and toenhance arrhythmia discrimination and therapy delivery performance.

FIG. 1A shows a typical report 10 of an arrhythmia logbook that presentsvarious data associated with a number of arrhythmic episodes. The datain FIG. 1A is sorted in a traditional manner by date and time of thearrhythmic episode. In the data shown in FIG. 1A, the implantablecardiac device from which the data was obtained implemented a number ofdifferent algorithms when discriminating between normal and arrhythmiccardiac rhythms, four of which are shown in FIG. 1A. In the particularexample illustrated in FIG. 1A, data associated with four discriminationalgorithms is provided in columns 12, 14, 16, and 18.

The first column 12 of discriminator data represents a Boolean output(True “T” or False “F”) from a “V>A” discriminator, which comparesventricular and atrial rates to determine if the ventricular rateexceeds the atrial rate plus a bias factor, such as 10 bpm. The secondcolumn 14 of discriminator data represents the stability of aventricular rhythm, which is shown in terms of milliseconds. The thirdcolumn 16 of discriminator data represents a Boolean output from an“A>Afib” discriminator, which compares the atrial rate to an atrialfibrillation threshold, such as 200 bpm for example. This discriminatoris used to inhibit therapy when a ventricular tachycardia is due toatrial fibrillation. The fourth column 18 of discrimination datarepresents the onset character of a ventricular cardiac rhythm, such assudden or gradual onset, and is used to differentiate sinus tachycardiafrom ventricular tachycardia. This data is represented in terms of apercentage.

The discriminators and associated data depicted in FIG. 1A arerepresentative of known algorithms that are used to evaluate cardiacarrhythmias and identify arrhythmic episodes that require therapy. It isunderstood that many other discriminators known in the art may beemployed in an implantable cardiac device and that a validation metricand methods of using same in accordance with the present invention maybe implemented in an implantable cardiac device that employs such otherknown discriminators. A validation metric and methods of using same inaccordance with the present invention may also be employed inpatient-external devices and systems that operate on data associatedwith various known discriminators.

In addition to assessing the data associated with the fourdiscriminators shown in FIG. 1A, the state of programmable settings ofsuch discriminators when the data was acquired is also important. As isshown in FIG. 2A, each of the four discriminators whose data is shown inFIG. 1A can be programmed OFF or ON, and each typically has a range ofprogrammable setting values. FIG. 2A is a device treatment decisiontable which is a mapping of discriminator activation state (shown alongupper horizontal side), discriminator outcome state (shown along leftvertical side), and treatment rules (shown along lower horizontal side).

When attempting to evaluate cardiac arrhythmia data, a clinician istypically provided with a list of episodes on an arrhythmia logbookreport retrieved from an implantable cardiac device by way of aprogrammer, as is shown in FIG. 1A. This list is typically organized bydate and type of episode. Columns may also be provided that display thehighest rate achieved during an episode and whether therapy wasdelivered. While this information presents a helpful snapshot of thepatient's episode history, date, type, and high rate, such informationis typically not sufficient to expeditiously prioritize the list forassessing device functionality and patient arrhythmia classifications.Additionally, for patients with multiple treated episodes, theindication of treatment also does not facilitate episode prioritization.

More detailed information concerning an episode is typically provided onan episode detail report. This report presents the calculationsassociated with each discriminator. Analyzing this very detailedinformation is often helpful when deciding to fully investigate aspecific episode. However, such detailed information does not assist theclinician in identifying which episodes should be selected to bestassess the device and the patient. In a typical follow-up clinicalsetting, a clinician (e.g., a physician and/or device manufacturerrepresentative) are often time-constrained by limited patient visitswhich are typically scheduled 15 minutes apart, making a thoroughevaluation of each arrhythmic event impractical.

In the absence of information that would enable a clinician toprioritize a list of arrhythmia episodes, many clinicians, who do notsystemically check each episode, randomly select a few episodes toinvestigate. Random selection, as a method, has been found to begenerally effective in the field, and, as a result, is widely practiced.However, random selection processes, by definition, can often result inoverlooking episodic data of importance, which may go unevaluated.

It can be appreciated that assessing cardiac rhythms based on multiplediscriminator data and programmable device settings can be relativelycomplex and time consuming, particularly when evaluating such dataduring routine follow-up patient visits of limited duration with aclinician. Ironically, although increases in device sophistication hasgenerally led to improved diagnosis and treatment of cardiacarrhythmias, such increased sophistication has made it more difficultand time consuming for the clinician to evaluate the appropriateness ofdevice functionality and decision-making processes.

A validation metric of the present invention represents a single indexthat can be used to rapidly identify possibly misclassified tachycardiaepisodes. For example, a validation metric value falling within apredetermined range or exceeding a predetermined threshold can be usedto identify tachycardia episodes that require evaluation, and prompt theclinician to further investigate such episodes. A validation metric ofthe present invention effectively reveals the level of confidence thatthe implantable cardiac device properly classified detected cardiacarrhythmias. A validation metric can serve to help the clinicianprioritize episodes for troubleshooting and to more quickly andaccurately determine the operating status/proper functionality of thedevice and the patient's clinical event history. A validation metric canalso be used to validate device diagnoses and the appropriateness oftherapy decisions. In this regard, a validation metric can effectivelyverify that the rhythm discriminator algorithms implemented in aparticular implantable cardiac device are properly classifying cardiacarrhythmias.

According to various embodiments, a validation metric of the presentinvention defines a probability of incorrectly classifying a cardiacarrhythmic episode, such as a ventricular tachycardia episode, as onethat requires treatment. A validation metric is reflective of theaccuracy by which each arrhythmia discrimination algorithm implementedby an implantable cardiac device properly classified an arrhythmicepisode. For example, a validation metric is preferably based on aproduct of the probabilities of the arrhythmia discrimination algorithmsimplemented by an implantable cardiac device to incorrectly classify anarrhythmic episode. Identifying potentially misclassified arrhythmicepisodes typically involves comparing validation metric values ofclassified cardiac arrhythmic episodes to a threshold or a thresholdrange.

By way of example, a validation metric of the present invention may becomputed based on the probability that an arrhythmia discriminator issatisfied as “treat for ventricular tachycardia,” but the underlyingrhythm is actually supraventricular tachycardia (SVT) or is distorted bynoise. The probability of inaccurately identifying ventriculartachycardia (VT) for a given discrimination algorithm can be expressedas:

Probability of inaccurately identifying VT=1−(probability of accuratelyidentifying VT)

For example, if a given arrhythmia discriminator has a probability of85% for accurately identifying a rhythm as VT, then it also has a 15%probability (i.e., 1−0.85=0.15) of inaccurately identifying the rhythmas VT. This 15% probability accounts for device errors due to welldocumented and understood sources, such as electrogram noise. Avalidation metric is preferably calculated by taking the individualprobabilities of each arrhythmia discrimination algorithm employed bythe implantable cardiac device and multiplying these probabilities toproduce a combined probability.

Conventional discrimination algorithms generally rely on thresholds(e.g., all-or-nothing scenarios such as whether or not the atrial rateis >200 bpm) to classify rhythms and/or the application/inhabitation oftherapy. In contrast, a validation metric of the present inventionrepresents a quantitative measure of device confidence in its rhythmclassification and/or therapy decisions based on a dynamic range ofprobabilities derived from a continuum of discriminator output values.

FIG. 2B shows a device treatment decision table similar to that of FIG.2A. However, the treatment decision matrix of FIG. 2A has been replacedwith a matrix of validation metrics. As can be seen in FIG. 2B, the morearrhythmia discriminator algorithms that are satisfied by the rhythmdetected by the device, the lower the validation metric value willbecome. The lower the value of the validation metric, the lower theprobability that the device has inaccurately identified ventriculartachycardia. In other words, one can be increasingly confident of thedevice's determination of ventricular tachycardia and its administrationof therapy as the metric value becomes lower.

FIG. 1B shows a report 20 of an arrhythmia logbook that presents variouscardiac rhythm data and a validation metric associated with each of thearrhythmic episodes shown in report 20. As is readily seen by acomparison of the reports 10 and 20 shown in FIGS. 1A and 1B,respectively, presentation of a single validation metric (shown incolumn 22 as VTI or ventricular tachycardia index), in contrast to themultiplicity of columnar data for four discriminators, greatlysimplifies the characterization of each arrhythmic episode. The datapresented in the report 20 may be sorted based on validation metricvalues to allow for quick identification of episodic data requiringfurther evaluation by a clinician.

FIG. 2C is a partial showing of an arrhythmia logbook report 20 withdata presented based on a validation metric sort. The data of column 24of report 20 represents validation metric values computed for each rowof arrhythmic episode data shown in report 20. Column 26 represents the“treat” (trt) or “no-treat” (blank) decision made by use of aconventional treatment decision table, such as that shown in FIG. 2A.Column 28 represents the outcome of voting to treat (i.e., voting todeliver therapy) by each of the four discriminators depicted in FIG. 2C.For example, a vote value of 3 indicates that 3 of the 4 discriminatorsvoted to treat the subject arrhythmia. The decision to treat or inhibittreatment is typically based on a weighted voting scheme.

As is discussed above, lower validation metric values are indicative ofhigher probabilities that the device correctly classified a cardiacarrhythmia for treatment. In general, validation metric values lowerthan about 0.10 indicate a relatively high probability that the devicecorrectly classified a cardiac arrhythmia. Validation metric values inexcess of about 0.10 indicate a relatively higher probability that thedevice may have incorrectly classified a cardiac arrhythmia. Forexample, episodes having metric values in excess of 0.20 that wereindicated as treated by the device are possible candidates of falsepositive assessments made by the device.

The validation metric data in region A of report 20 indicates that theepisodes are likely appropriately classified, and that treatment of samewas appropriate. It can be seen that the validation metric values incolumn 24 of region A are relatively low, indicating a relatively highprobability that the device properly classified the cardiac rhythm. Thevoting data in column 28 of region A corroborates this outcome. Thecurrent treatment decision data in column 26 of region A indicates thattherapy was properly delivered to treat each of the arrhythmiasassociated with the region A data.

The validation metric values in column 28 of region B are greater thanthose in region A, indicating an increased likelihood that the devicemay have improperly classified a given rhythm. In general, thevalidation metric values in region B are relatively low. However, thevoting data in column 28 for region B indicates a reduced number ofvotes to treat. A review of the data in region B suggests that one ofthe rhythms (the rhythm having a validation metric value of 0.10 andvoting value of 1) for which treatment was delivered is quite likelysuspect—a likely false positive. This rhythm has an indication oftreatment delivery with a relatively high validation metric value, andlikely represents a borderline scenario for treatment which warrantsfurther investigation.

Region B is populated by data for several episodes that have relativelyincreased validation metric values, a reduced number of votes to treatas can be seen in column 28, and an indication of treatment delivery.These episodes in region B are good candidates for further evaluation ofdevice functionality and/or decision making criteria.

The data in region C reveals relatively high validation metric values incolumn 24 for rhythms that were not subject to treatment. Because thehigher validation metric values indicate an increasing probability thatthe device improperly classified the rhythms in region C, these episodeswould warrant further investigation if there were a correspondingindication of treatment delivery in column 26. In the illustrativeexample shown in FIG. 2C, none of the episodes in region C were subjectto treatment, and, therefore, would not necessarily be of particularinterest for further evaluation.

The data shown in FIG. 2C illustrates how a validation metric can beused to facilitate rapid evaluation of data for a large number ofarrhythmic episodes. A validation metric of the present inventionreveals which episodes should be subject to further evaluation by aclinician and does so in a manner that is easily perceptible and readilyunderstandable. To increase data perception, colors or other indicia maybe incorporated into the data presentation. For example, the regionsshown in FIG. 2C may be colorized in a manner that connotes theirrelative probability of classification accuracy. For example, region Amay be colored “green” to indicate episodes that are likely properlyclassified. Region B may be colored “yellow” to indicate episodes thatmay necessitate further review, particularly those that have anindication of therapy delivery. Region C may be colored “red” toindicate episodes that require further investigation where there is anindication of treatment delivery for such episodes.

A validation metric approach of the present invention may be used incurrent and future cardiac devices that use a multiplicity of rhythmdiscrimination or classification algorithms. Validation metric valuescan be added to diagnostic and therapy evaluation reports, such as acolumn on an arrhythmia logbook report, and could replace themultiplicity of columnar data currently occupied by individualdiscriminator data. Accordingly, clinicians could be exposed to thedetail of the multiplicity of discriminator data after the validationmetric has guided them to the potentially problematic episodes. Sincethe validation metric effectively incorporates multiple columns ofdiscriminator data into a single column, it has the potential to bequickly and widely used since clinicians are likely to readilyunderstand what the metric is attempting to demonstrate, particularly toless sophisticated clinicians.

According to one implementation, a validation metric-baseddiscrimination algorithm is initially available on an external processoror system, such as a programmer or a remote server-based APM system. Thevalidation metric-based discrimination algorithm may be transferred fromthe external processor/system to the implantable cardiac device.Thereafter, the processor of the implantable device may implement thevalidation metric-based discrimination algorithm. It is understood thatthe validation metric-based discrimination algorithm may be implementedby the external system only, by the implantable cardiac device only or,preferably, by both the patient-internal and patient-externaldevices/systems.

FIG. 3 is a flow chart that provides a general characterization of avalidation metric-based discrimination algorithm of the presentinvention. Data for a number of cardiac arrhythmic episodes are provided301 to the processor of the implantable cardiac device or an externalprocessor, such as that of a programmer or an APM system. A validationmetric is computed 303 for each episode. As discussed above, thevalidation metric represents a measure by which an arrhythmia wasproperly (or improperly) classified by the discrimination algorithmsimplemented by the implantable cardiac device. Potentially misclassifiedepisodes are algorithmically identified 305 to assess whether deliveryof therapy or withholding of same was proper or improper.

The outcome from this assessment may be used to trigger additionalprocesses, such as modification of a therapy parameter or generation ofa template in the context of a morphology-based discriminationalgorithm. The outcome from this assessment may also be used for variousalerting and reporting functions, such as notifying a physician ofdiscordance between classifications made by the implantable device andexternal device, respectively. These and other processes may beimplemented automatically, by the physician, or by cooperation ofautomatic and physician-assisted processes.

A validation metric of the present invention may be used in a variety ofmanners, including validation of implantable cardiac deviceclassification and/or therapy decisions by an external system. Manyimplantable medical devices employ multiple detection algorithms toenhance classification of cardiac rhythms. The sophistication of suchdetection algorithms is typically limited by the computational resourcesof the implantable cardiac device. This computational limitation can beoffset by use of virtually unlimited computational resources of systemsavailable externally of the patient.

Due to limited computations resources, conventional tachyarrhythmiadetection algorithms have used rate, stability, and onset todiscriminate between ventricular tachycardia (VT) and supraventriculartachycardia (SVT). Morphology-based algorithms represent an improvementon these conventional algorithms, but make the assumption thatarrhythmias not originating in the ventricle have electrogrammorphologies similar to conducted normal sinus rhythm, which may or maynot be accurate. The addition of physician-identified SVT templates formorphology-based detection algorithms has been shown to improvemorphology-based rhythm identification (RID) algorithm performance, butresults in increased physician burden. Use of a validation metric ofother validation algorithm may be used to automate SVT templategeneration using patient-external computing resources, such as remoteAPM systems.

FIG. 4A is a flow chart that characterizes a morphology-based rhythmidentification algorithm according to embodiments of the presentinvention. According to this embodiment, two types of morphologicaltemplates are used to evaluate cardiac rhythms. A first routine of thealgorithm shown in FIG. 4A compares cardiac rhythms to an NSR (normalsinus rhythm) template 304. Such an NSR template is typically generatedby the implantable cardiac device, and may be updated over time.

A second routine of the algorithm shown in FIG. 4A compares cardiacrhythms to one or more SVT templates 308. Such SVT templates 308 aretypically generated in response to physician analysis of device data.The physician is typically required to review stored electrograms thathave a supraventricular origin, and determine if indeed suchelectrograms are representative of SVT rhythms. The physician may theninitiate generation of an SVT template, via a programmer or APM system,which is subsequently transferred to the implantable cardiac device. TheSVT template may then be used in the second comparison routine shown inboxes 306, 308 in FIG. 4A. FIG. 4B depicts the use ofphysician-generated SVT templates in an extended decision tree that isenabled after a first physician-generated SVT template is transferred tothe implantable cardiac device.

With continued reference to FIG. 4A, arrhythmic episodes are detected bythe implantable cardiac device, and a number of morphological featuresof each beat are compared to an NSR (normal sinus rhythm) template 304.Comparisons are made between each feature of a given beat and acorresponding feature of the NSR template 304. A mathematicalcorrelation coefficient, referred to as a feature correlationcoefficient (FCC), is computed for the features of each beat. If asufficient number of beats in a sequence of beats have FCC values thatexceed a predefined threshold (e.g., FCC not <0.94 or, in other words,FCC>0.94), as tested at box 302, then the rhythm is classified as an SVTrhythm 312.

If a sufficient number of beats in a sequence of beats have FCC valuesthat fail to exceed a predefined threshold (e.g., FCC<0.94), as testedat box 302, then a secondary or extended evaluation of the rhythms ismade using an SVT template 308. It is assumed that SVT template 308 wasgenerated by a physician and transferred to the implantable cardiacdevice in a manner discussed above. If a sufficient number of beats in asequence of beats have FCC values that exceed a predefined threshold(e.g., FCC not<0.95 or, in other words, FCC>0.95), as tested at box 306,then the rhythm is classified as an SVT rhythm 312. If, however, asufficient number of beats in a sequence of beats have FCC values thatfail to exceed a predefined threshold (e.g., FCC<0.95), as tested at box306, then the rhythm is classified as a VT rhythm 310.

A morphology-based detection approach, such as that shown in FIGS. 4Aand 4B, and as discussed above, may be implemented in accordance withthe disclosures of commonly owned U.S. Patent Publication Nos.2006/0111643 and 2006/0074331; U.S. patent application Ser. No.11/312,280 filed Dec. 20, 2005 under Attorney Docket No. GUID.229PA; andU.S. Pat. No. 6,449,503, all of which are hereby incorporated herein byreference.

FIG. 5 illustrates a method of improving the performance of amorphology-based tachyarrhythmia discrimination algorithm implemented byan implantable cardiac device by use of patient-external computationalresources. According to the embodiment shown in FIG. 5, arrhythmicepisodes are evaluated 402, 404 by use of a morphology-basedtachyarrhythmia discrimination algorithm, shown as a rhythmidentification algorithm. As previously discussed, the RID algorithmuses NSR and SVT templates 404 for diagnosis of arrhythmias andtreatment of same. Following a tachyarrhythmia event, the implantablecardiac device uploads 406 all relevant data for the event to anexternal processor or server (e.g., an APM system processor/server). Therelevant data may include electrograms from all available channels(e.g., right atrium, right ventricle, shock or far-field channels) andtiming information (e.g., rate, stability, onset).

An external discrimination algorithm (EDA) is implemented by theexternal system (e.g., APM server) to evaluate 420 the arrhythmic eventor episode. The EDA is preferably a blinded algorithm implemented by theAPM server to analyze and classify the rhythms implicated in theuploaded data. Preferably, all arrhythmic events are recorded and storedon the APM server.

If the discrimination algorithm implemented on the APM server is thesame algorithm as used in the implantable cardiac device, the event isnoted as such. It is desirable to use a discrimination algorithm thatdiffers from those employed by the implantable cardiac device. In oneembodiment, for example, the EDA is implemented using a validationmetric methodology described hereinabove.

In another embodiment, the EDA is implemented as an enhanced rhythmidentification discrimination algorithm. In general, the number ofelectrogram features subject to FCC analysis by the implantable cardiacdevice is limited by the limited computational resources of theimplantable device platform. A typical RID analysis involves comparisonof some 8 or 10 morphological features for detected electrogramwaveforms and VT and/or SVT templates. An EDA analysis need not belimited to the number of features analyzed by the implantable cardiacdevice. For example, the EDA analysis may involve in excess of 20morphological features, such as up to 100 or more features.

Moreover, more sophisticated and/or computationally intensivecorrelation techniques may be employed by the EDA (or plural EDAs) toincrease the probability of properly classifying a given rhythm by theEDA. Other discrimination algorithms in addition to RID may beimplemented as EDAs to verify or corroborate the outcome of rhythmassessments by RID or other primary discrimination algorithm(s). Suchother discrimination algorithms may include rate-based, pattern-based,or rate and pattern-based discrimination algorithms as are known in theart. Each of these discrimination algorithms may be modified to enhancerhythm classification robustness in view of the virtually unlimitedcomputational resources made available by external systems.

According to a further embodiment, the EDA may be implemented to performwavelet analysis when comparing detected rhythm waveforms and templatewaveforms. For example, digitized cardiac signals may be analyzed byfirst transforming the signals into signal wavelet coefficients using awavelet transform. Higher amplitude signal wavelet coefficients may beidentified and compared with a corresponding set of template waveletcoefficients derived from signals indicative of a heart depolarizationof known type (e.g., NSR, VT, SVT templates/signals). The digitizedsignals may be transformed using a Haar wavelet transform to obtain thesignal wavelet coefficients, and the transformed signals may be filteredby deleting lower amplitude signal wavelet coefficients.

The transformed signals may be compared by ordering the signal andtemplate wavelet coefficients by absolute amplitude and comparing theorders of the signal and template wavelet coefficients. Alternatively,the transformed signals may be compared by calculating distances betweenthe signal and wavelet coefficients. Details of suitable waveletanalysis techniques that may be implemented by an EDA of the presentinvention are disclosed in U.S. Pat. No. 6,393,316, which is herebyincorporated herein by reference.

In accordance with another embodiment, rhythm discrimination may bebased on a morphological analysis of the area under the peaks ofdetected cardiac waveforms and known template waveforms (e.g., NSR, VT,SVT template waveforms). For example, a template based on morphology ofa normal sinus rhythm may be collected. A sensed cardiac signal iscompared against the template to determine how closely the sensed andtemplate signals correspond based on morphology. The comparison may bedone based on peak information in the template and the sensed signal.

A score may be generated to indicate the degree of similarity betweenthe template and the sensed signal. Peak information may be extracted inthe following manner. A group of three consecutive peaks having alargest cumulative peak amplitude is located in the template and in thesensed signal. The polarity, position and area of each peak within thegroup is then determined. The area of each peak is normalized. Thepolarities, positions and normalized areas represent the peakinformation that is used for comparison. Details of suitable area-basedmorphological analysis techniques that may be implemented by an EDA ofthe present invention are disclosed in U.S. Pat. No. 5,779,645, which ishereby incorporated herein by reference.

With further reference to FIG. 5, if the discrimination algorithmimplemented on the APM server determines, to a specified degree ofcertainty (certainty X and Y shown in blocks 424 and 426) that the eventwas incorrectly classified by the implantable cardiac device, then theclassification of the event as determined by the APM server-baseddiscrimination algorithm may be used to modify 440 the discriminationalgorithm (e.g., one or more parameters and/or templates) implemented bythe implantable cardiac device. For example, if the APM-server's EDAclassifies a given rhythm as SVT rather than VT (as was classified bythe implantable cardiac device), then the APM server would update theSVT template 440 in the implantable device to reflect the newclassification and notify the physician that an update has been made tothe SVT, template.

Alternatively, the APM server could notify the physician 430 that theevent was incorrectly classified by the implanted device and ask thephysician whether the SVT template should be updated accordingly. If andwhen the physician responds affirmatively, the APM server-basedalgorithm then automatically updates the SVT template 434, 440. If theAPM server-based algorithm cannot classify the event, then the physicianwould be notified and physician intervention would be required.

The methodology depicted in FIG. 5 provides for external computationalresources that can be leveraged to automatically classify a patient'stachyarrhythmia events off-line using an algorithm(s) that is/are toocomputationally intensive to be run on an implantable device platform.Automatic classification can then be used to update morphologicaltemplates in order to improve the performance of morphology-based VT/SVTdiscrimination algorithms. In addition, an external discriminationalgorithm implemented by an APM sever or other external processor can beused to validate an implantable device's classification of atachyarrhythmia and notify the physician if discrepancies exist betweenthe device classification and a remote system classification. Anotheruseful function involves notifying the physician of the presence ofanomalous rhythms which cannot be automatically classified by either thedevice or a remote system.

Referring now to FIG. 6, there is shown a cardiac rhythm management(CRM) system that may be used to implement rhythm discrimination and/orvalidation in accordance with the approaches of the present invention.The CRM system in FIG. 6 includes a pacemaker/defibrillator 600 enclosedwithin a housing and coupled to a lead system 602. The housing and/orheader of the pacemaker/defibrillator 600 may incorporate one or morecan or indifferent electrodes 608, 609 used to provide electricalstimulation energy to the heart and/or to sense cardiac electricalactivity. The pacemaker/defibrillator 600 may utilize all or a portionof the device housing as a can electrode 608. Thepacemaker/defibrillator 600 may include an indifferent electrode 609positioned, for example, on the header or the housing of thepacemaker/defibrillator 600. If the pacemaker/defibrillator 600 includesboth a can electrode 608 and an indifferent electrode 609, theelectrodes 608, 609 typically are electrically isolated from each other.

The lead system 602 is used to detect cardiac electrical signalsproduced by the heart and to provide electrical energy to the heartunder certain predetermined conditions to treat cardiac arrhythmias. Thelead system 602 may include one or more electrodes used for pacing,sensing, and/or defibrillation. In the embodiment shown in FIG. 6, thelead system 602 includes an intracardiac right ventricular (RV) leadsystem 604, an intracardiac right atrial (RA) lead system 605, and anintracardiac left ventricular (LV) lead system 606. An extracardiac leftatrial (LA) lead system 607 is employed.

The CRM system illustrated in FIG. 6 is configured for biventricular orbiatrial pacing. The lead system 602 of FIG. 6 illustrates oneembodiment that may be used in connection with the rhythm discriminationand/or validation processes described herein. Other leads and/orelectrodes may additionally or alternatively be used. For example, theCRM system may pace multiple sites in one cardiac chamber via multipleelectrodes within the chamber. This type of multisite pacing may beemployed in one or more of the right atrium, left atrium, rightventricle or left ventricle. Multisite pacing in a chamber may be usedfor example, to increase the power and or synchrony of cardiaccontractions of the paced chamber.

The lead system 602 may include intracardiac leads 604, 605, 606implanted in a human body with portions of the intracardiac leads 604,605, 606 inserted into a heart. The intracardiac leads 604, 605, 606include various electrodes positionable within the heart for sensingelectrical activity of the heart and for delivering electricalstimulation energy to the heart, for example, pacing pulses and/ordefibrillation shocks to treat various arrhythmias of the heart.

As illustrated in FIG. 6, the lead system 602 may include one or moreextracardiac leads 607 having electrodes 615, 618, e.g., epicardialelectrodes, positioned at locations outside the heart for sensing andpacing one or more heart chambers. In some configurations, theepicardial electrodes may be placed on or about the outside of the heartand/or embedded in the myocardium from locations outside the heart. Theright ventricular lead system 604 illustrated in FIG. 6 includes anSVC-coil 616, an RV-coil 614, an RV-ring electrode 611, and an RV-tipelectrode 612. The right ventricular lead system 604 extends through theright atrium and into the right ventricle.

In particular, the RV-tip electrode 612, RV-ring electrode 611, andRV-coil electrode 614 are positioned at appropriate locations within theright ventricle for sensing and delivering electrical stimulation pulsesto the heart. The SVC-coil 616 is positioned at an appropriate locationwithin the right atrium chamber of the heart or a major vein leading tothe right atrial chamber.

In one configuration, the RV-tip electrode 612 referenced to the canelectrode 608 may be used to implement unipolar pacing and/or sensing inthe right ventricle. Bipolar pacing and/or sensing in the rightventricle may be implemented using the RV-tip 612 and RV-ring 611electrodes. In yet another configuration, the RV-ring 611 electrode mayoptionally be omitted, and bipolar pacing and/or sensing may beaccomplished using the RV-tip electrode 612 and the RV-coil 614, forexample. The right ventricular lead system 604 may be configured as anintegrated bipolar pace/shock lead. The RV-coil 614 and the SVC-coil 616are defibrillation electrodes.

The left ventricular lead 606 includes an LV distal electrode 613 and anLV proximal electrode 617 located at appropriate locations in or aboutthe left ventricle for pacing and/or sensing the left ventricle. Theleft ventricular lead 606 may be guided into the right atrium of theheart via the superior vena cava. From the right atrium, the leftventricular lead 606 may be deployed into the coronary sinus ostium, theopening of the coronary sinus 650. The lead 606 may be guided throughthe coronary sinus 650 to a coronary vein of the left ventricle. Thisvein is used as an access pathway for leads to reach the surfaces of theleft ventricle which are not directly accessible from the right side ofthe heart. Lead placement for the left ventricular lead 606 may beachieved via subclavian vein access and a preformed guiding catheter forinsertion of the LV electrodes 613, 617 adjacent to the left ventricle.

Unipolar pacing and/or sensing in the left ventricle may be implemented,for example, using the LV distal electrode referenced to the canelectrode 608. The LV distal electrode 613 and the LV proximal electrode617 may be used together as bipolar sense and/or pace electrodes for theleft ventricle. The lead system 602 in conjunction with thepacemaker/defibrillator 600 may provide bradycardia pacing therapy tomaintain a hemodynamically sufficient heart rate. The left ventricularlead 606 and the right ventricular lead 604 and/or the right atrial leadand the left atrial lead may be used to provide cardiacresynchronization therapy such that the ventricles and/or atria of theheart are paced substantially simultaneously or in phased sequenceseparated by an interventricular or interatrial pacing delay, to provideenhanced cardiac pumping efficiency for patients suffering fromcongestive heart failure.

The right atrial lead 605 includes a RA-tip electrode 656 and an RA-ringelectrode 654 positioned at appropriate locations in the right atriumfor sensing and pacing the right atrium. In one configuration, theRA-tip 656 referenced to the can electrode 608, for example, may be usedto provide unipolar pacing and/or sensing in the right atrium. Inanother configuration, the RA-tip electrode 656 and the RA-ringelectrode 654 may be used to effect bipolar pacing and/or sensing.

Referring now to FIG. 7, there is shown a block diagram of an embodimentof an implantable CRM system 700 suitable for implementing rhythmdiscrimination and/or validation approaches of the present invention.FIG. 7 shows a CRM system 700 divided into functional blocks. It isunderstood by those skilled in the art that there exist many possibleconfigurations in which these functional blocks can be arranged. Theexample depicted in FIG. 7 is one possible functional arrangement. Otherarrangements are also possible. For example, more, fewer or differentfunctional blocks may be used to describe a cardiac system suitable forimplementing rhythm discrimination and/or validation processes of thepresent invention. In addition, although the CRM system 700 depicted inFIG. 7 contemplates the use of a programmable microprocessor-based logiccircuit, other circuit implementations may be utilized.

The CRM system 700 includes a control processor 740 capable ofcontrolling the delivery of pacing pulses or defibrillation shocks tothe right ventricle, left ventricle, right atrium and/or left atrium.The pacing therapy circuitry 730 is configured to generate pacing pulsesfor treating bradyarrhythmia, for example, or for synchronizing thecontractions of contralateral heart chambers using biatrial and/orbiventricular pacing.

The CRM system 700 includes arrhythmia discrimination circuitry 715configured to classify cardiac rhythms, such as by use of rate-basedand/or morphological-based algorithms operating on detected cardiacsignals. The arrhythmia discrimination circuitry 715 typically operatesto detect atrial and/or ventricular tachyarrhythmia or fibrillationusing a multiplicity of discrimination algorithms. Rhythm discriminationcircuitry 715 or the control processor 740 may be configured toimplement a validation metric-based discrimination analysis 720 asdescribed above. Under control of the control processor 740, thepacing/cardioversion/defibrillation circuitry 735 is capable ofgenerating high energy shocks to terminate the tachyarrhythmia episodes(e.g., antitachycardia pacing (ATP), cardioversion, and defibrillationtherapies), as determined by the arrhythmia discriminator circuitry 715and verified by the validation metric-based analysis.

The pacing pulses and/or defibrillation shocks are delivered viamultiple cardiac electrodes 705 electrically coupled to a heart anddisposed at multiple locations within, on, or about the heart. One ormore electrodes 705 may be disposed in, on, or about each heart chamberor at multiple sites of one heart chamber. The electrodes 705 arecoupled to switch matrix 725 circuitry that is used to selectivelycouple the electrodes 705 to the sense circuitry 710 and the therapycircuitry 730, 735.

The CRM system 700 is typically powered by an electrochemical battery(not shown). A memory 745 stores data (electrograms from multiplechannels, timing data, etc.) and program commands used to implement therhythm discrimination and/or validation approaches described hereinalong with other features. Data and program commands may be transferredbetween the CRM system 700 and a patient-external device 755 viatelemetry-based communications circuitry 750.

The patient-external device 755 may be implemented as a programmer, anAPM system or other external computational resource. A display 757 of auser interface of the patient-external device 755 is typically providedto facilitate user interaction with the patient-external device 755 andimplantable cardiac device. Data transferred from the implantablecardiac device to the patient-external device 755 may be organized forpresentation in an arrhythmia logbook format as discussed previously. Aclinician or physician may review electrograms and related data fortroubleshooting and adjusting device programming. A physician may reviewelectrograms on the display 757 and generate SVT templates via a userinterface of the patient-external device 755 in a manner describedhereinabove.

The components, functionality, and structural configurations depictedherein are intended to provide an understanding of various features andcombination of features that may be incorporated in an implantablecardiac device, such as a pacemaker/defibrillator. It is understood thata wide variety of cardiac monitoring and/or stimulation deviceconfigurations are contemplated, ranging from relatively sophisticatedto relatively simple designs. As such, particular cardiac deviceconfigurations may include particular features as described herein,while other such device configurations may exclude particular featuresdescribed herein.

Various modifications and additions can be made to the preferredembodiments discussed hereinabove without departing from the scope ofthe present invention. Accordingly, the scope of the present inventionshould not be limited by the particular embodiments described above, butshould be defined only by the claims set forth below and equivalentsthereof.

1. A method for identifying cardiac arrhythmias that are potentiallymisclassified by a cardiac device implanted in a patient, theimplantable cardiac device configured to classify cardiac arrhythmiasusing a plurality of arrhythmia discrimination algorithms, the methodcomprising: providing data associated with a plurality of cardiacarrhythmic episodes for which a cardiac electrical therapy was deliveredor withheld by the implantable cardiac device based on the plurality ofarrhythmia discrimination algorithms; computing a metric for each of thearrhythmic episodes, the metric defining a measure by which theimplantable cardiac device properly classified the arrhythmia; andalgorithmically identifying potentially misclassified arrhythmicepisodes of the plurality of cardiac arrhythmic episodes for whichcardiac electrical therapy was inappropriately delivered or withheldusing the metric.
 2. The method of claim 1, wherein the metric defines aprobability of incorrectly classifying a cardiac arrhythmic episode. 3.The method of claim 1, wherein the metric defines a probability ofincorrectly classifying a ventricular tachycardia episode.
 4. The methodof claim 1, wherein the metric is reflective of the accuracy by whicheach arrhythmia discrimination algorithm implemented by the implantablecardiac device properly classified an arrhythmic episode.
 5. The methodof claim 1, wherein the metric is based on a product of theprobabilities of the arrhythmia discrimination algorithms implemented bythe implantable cardiac device to incorrectly classify an arrhythmicepisode.
 6. The method of claim 1, wherein algorithmically identifyingpotentially misclassified arrhythmic episodes comprises comparing metricvalues of the plurality of cardiac arrhythmic episodes to a threshold ora threshold range.
 7. The method of claim 1, wherein the data andmetrics associated with the plurality of cardiac arrhythmic episodes arearranged in a logbook format, the method further comprising displayingthe potentially misclassified arrhythmic episodes for clinician review.8. The method of claim 7, further comprising varying the manner in whichthe data and metrics associated with the potentially misclassifiedarrhythmic episodes are displayed in accordance with a degree ofpotential misclassification.
 9. The method of claim 1, wherein the dataand metrics associated with the plurality of cardiac arrhythmic episodesare arranged in a logbook format, the method further comprising sortingthe data based on the metrics, and displaying the sorted data andmetrics for clinician review.
 10. The method of claim 1, whereincomputing the metric and algorithmically identifying potentiallymisclassified arrhythmic episodes are respectively performed by a deviceexternal to the patient.
 11. The method of claim 1, further comprisingadjusting a parameter of one or more of the arrhythmia discriminationalgorithms.
 12. The method of claim 1, wherein: the data is provided toa processor external to the patient; the external processor implementsone or more arrhythmia discrimination algorithms to classify the cardiacarrhythmias based on the data; and the metric represents a measure ofcertainty that the cardiac arrhythmia classification of arrhythmicepisodes respectively made by the implantable cardiac device and theexternal processor are in agreement.
 13. The method of claim 12, furthercomprising adjusting the metric in response to clinician input.
 14. Themethod of claim 12, further comprising flagging arrhythmic episodes forclinician review in response to the metric failing to exceed a certaintythreshold.
 15. The method of claim 12, further comprising modifying oneor more of the plurality of arrhythmia discrimination algorithms of theimplantable cardiac device in response to the metric failing to exceed acertainty threshold.
 16. The method of claim 12, wherein the pluralityof arrhythmia discrimination algorithms comprises an algorithm thatcompares a morphology of a cardiac signal to a supraventriculartachycardia (SVT) template, the method further comprising automaticallygenerating an SVT template for arrhythmic episodes for which the cardiacarrhythmia classifications respectively made by the implantable cardiacdevice and the external processor are in disagreement but the metricmeets or exceeds a certainty threshold.
 17. The method of claim 12,wherein the plurality of arrhythmia discrimination algorithms comprisesan algorithm that compares a morphology of a cardiac signal to asupraventricular tachycardia (SVT) template, the method furthercomprising: flagging arrhythmic episodes for clinician review inresponse to the metric failing to meet or exceed a certainty threshold;and generating an SVT template for selected ones of the flaggedarrhythmic episodes in response to a clinician input.
 18. A system foridentifying cardiac arrhythmias that are potentially misclassified,comprising: an implantable cardiac device configured to classify cardiacarrhythmias using a plurality of arrhythmia discrimination algorithms; amemory that stores data associated with a plurality of cardiacarrhythmic episodes for which a cardiac electrical therapy was deliveredor withheld by the implantable cardiac device based on the plurality ofarrhythmia discrimination algorithms; and a processor configured tocompute a metric for each of the arrhythmic episodes, the metricdefining a measure by which the implantable cardiac device properlyclassified the arrhythmia, the processor configured to identifypotentially misclassified arrhythmic episodes of the plurality ofcardiac arrhythmic episodes for which cardiac electrical therapy wasinappropriately delivered or withheld using the metric.
 19. The systemof claim 18, wherein at least one of the memory and the processor isprovided in the implantable cardiac device.
 20. The system of claim 18,wherein at least one of the memory and the processor is provided in apatient-external device.
 21. The system of claim 18, wherein the metricdefines a probability of incorrectly classifying a cardiac arrhythmicepisode.
 22. The system of claim 18, wherein the metric is reflective ofthe accuracy by which each arrhythmia discrimination algorithmimplemented by the implantable cardiac device properly classified anarrhythmic episode.
 23. The system of claim 18, wherein the metric isbased on a product of the probabilities of the arrhythmia discriminationalgorithms implemented by the implantable cardiac device to incorrectlyclassify an arrhythmic episode.
 24. The system of claim 18, wherein theprocessor is configured to identify potentially misclassified arrhythmicepisodes by comparing metric values of the plurality of cardiacarrhythmic episodes to a threshold or a threshold range.
 25. The systemof claim 18, wherein the data and metrics associated with the pluralityof cardiac arrhythmic episodes are organized in a logbook format in thememory, the system further comprising a display configured to displaythe potentially misclassified arrhythmic episodes for clinician review.26. The system of claim 18, wherein a parameter of one or more of thearrhythmia discrimination algorithm is adjusted in the implantablecardiac device based on the metric.
 27. The system of claim 18, whereinthe memory and the processor are disposed in a patient-external device,the processor configured to implement one or more arrhythmiadiscrimination algorithms to classify the cardiac arrhythmias based onthe data, wherein the metric represents a measure of certainty that thecardiac arrhythmia classification of arrhythmic episodes respectivelymade by the implantable cardiac device and the processor are inagreement.
 28. The system of claim 27, wherein the metric is adjusted bythe processor in response to clinician input.
 29. The system of claim27, wherein the processor is configured to flag arrhythmic episodes forclinician review in response to the metric failing to exceed a certaintythreshold.
 30. The system of claim 27, wherein one or more of theplurality of arrhythmia discrimination algorithms of the implantablecardiac device is modified at least in part by the processor in responseto the metric failing to exceed a certainty threshold.
 31. The system ofclaim 27, wherein the plurality of arrhythmia discrimination algorithmscomprises an algorithm that compares a morphology of a cardiac signal toa supraventricular tachycardia (SVT) template, the processor configuredto automatically generate an SVT template for arrhythmic episodes forwhich the cardiac arrhythmia classifications respectively made by theimplantable cardiac device and the processor are in disagreement but themetric meets or exceeds a certainty threshold.
 32. The system of claim27, wherein the plurality of arrhythmia discrimination algorithmscomprises an algorithm that compares a morphology of a cardiac signal toa supraventricular tachycardia (SVT) template, the processor configuredto flag arrhythmic episodes for clinician review in response to themetric failing to meet or exceed a certainty threshold and to generatean SVT template for selected ones of the flagged arrhythmic episodes inresponse to a clinician input.